Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach

• URL: http://arxiv.org/abs/2501.12624v1
• Date: Wed, 22 Jan 2025 04:12:32 GMT
• Title: Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach
• Authors: Huilin lai, Guang Zeng, Xunkai Li, Xudong Shen, Yinlin Zhu, Ye Luo, Jianwei Lu, Lei Zhu,
• Abstract summary: Federated graph learning (FGL) has emerged as a promising paradigm for collaborative machine learning. MHtFGL problem arises in real-world applications, such as the aggregation of models from different companies. We propose the Federated Graph Knowledge Collaboration (FedGKC) framework, comprising two key components.
• Score: 15.747994926672868
• License:
• Abstract: Federated graph learning (FGL) has emerged as a promising paradigm for collaborative machine learning, enabling multiple parties to jointly train models while preserving the privacy of raw graph data. However, existing FGL methods often overlook the model-centric heterogeneous FGL (MHtFGL) problem, which arises in real-world applications, such as the aggregation of models from different companies with varying scales and architectures. MHtFGL presents an additional challenge: the diversity of client model architectures hampers common learning and integration of graph representations. To address this issue, we propose the Federated Graph Knowledge Collaboration (FedGKC) framework, comprising two key components: Client-side Self-Mutual Knowledge Distillation, which fosters effective knowledge sharing among clients through copilot models; and Server-side Knowledge-Aware Model Aggregation, which enhances model integration by accounting for the knowledge acquired by clients. Experiments on eight benchmark datasets demonstrate that FedGKC achieves an average accuracy improvement of 3.74% over baseline models in MHtFGL scenarios, while also maintaining excellent performance in homogeneous settings.

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